Gene interactions observed with the HDL-c blood lipid, intakes of protein, sugar and biotin in relation to circulating homocysteine concentrations in a group of black South Africans
Bibliographic record
Abstract
Elevated homocysteine (Hcy) is associated with several pathologies. Gene–diet interactions related to Hcy might be used to customize dietary advice to reduce disease incidence. To explore this possibility, we investigated interactions between anthropometry, biochemical markers and diet and single-nucleotide polymorphisms (SNPs) in relation to Hcy concentrations. Five SNPs of Hcy-metabolizing enzymes were analyzed in 2010 black South Africans. Hcy was higher with each additional methylenetetrahydrofolate reductase (MTHFR) C677T minor allele copy, but was lower in methionine synthase (MTR) 2756AA homozygotes than heterozygotes. Individuals harboring cystathionine β synthase (CBS) 833 T/844ins68 had lower Hcy concentrations than others. No interactive effects were observed with any of the anthropometrical markers. MTHFR C677T and CBS T833C/844ins68 homozygote minor allele carriers presented with lower Hcy as high density lipoprotein cholesterol (HDL-c) increased. Hcy concentrations were negatively associated with dietary protein and animal protein intake in the TT and TC genotypes, but positively in the CC genotype of CBS T833C/844ins68. Hcy was markedly higher in TT homozygotes of MTHFR C677T as added sugar intake increased. In CBS T833C/844ins68 major allele carriers, biotin intake was negatively associated with Hcy; but positively in those harboring the homozygous minor allele. The Hcy–SNP associations are modulated by diet and open up the possibility of invoking dietary interventions to treat hyperhomocysteinemia. Future intervention trials should further explore the observed gene–diet and gene–blood lipid interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".